Many multi-element systems include a large number of discrete devices which provide monitoring and control functions for their respective systems. For instance, a nuclear power plant depends upon plant instrumentation for accurate reporting of equipment status and thermofluid conditions. In critical multi-element systems, such as nuclear power plants, it is imperative to obtain accurate readings from the elements within the system. Erroneous or contradictory instrument signals may provide the operator or automatic control system with incorrect information which may result in dangerous system operation.
Measurement errors can arise from drift in instrument calibration or from instrument failure. Errors due to calibration drift are not easy to detect because they are generally associated with a gradual degradation of the measurement device.
Instrument calibration typically requires manual intervention, which is a time consuming operation. Within complex multi-element systems, a significant portion of the operation and maintenance budget is consumed by the instrument recalibration activities. Aside from the expense associated with present instrument calibration techniques, the manual nature of the operation is susceptible to the introduction of errors.
In many complex multi-element systems, such as nuclear power plants, those measurements which are critical to safety are subject to hardware redundant measurements. In other words, a number of redundant instruments are used to measure the same value. Signal validation techniques are then used to determine the most accurate reading from the acquired redundant data. Many of these techniques rely upon software to determine the most accurate value. They are also capable of detecting a failure or a significant drift.
Not all system measurements justify redundant instruments. Therefore, many parameters have a single instrument. As a result, periodic calibration testing is currently the only means for detecting failures and instrument drifts on non-redundant instruments.
In contrast to the single instrument and redundant instrument techniques described, mathematical redundancy techniques may also be employed. Mathematical redundancy methods can be subdivided into analytical methods and statistical methods.
In the analytical redundancy method, a computer model is used to represent the relationship between a target instrument reading, readings from other instruments, a set of model inputs, and an output. For instance, in a nuclear power plant, the reactor core exit temperature can be described as a function of primary coolant flowrate, reactor power, inlet core temperature, primary coolant pressure, and a heat transfer function. Once these interrelationships are defined, a reference value for core exit temperature can be computed from the other variable measurements.
In the analytical redundancy technique, the model is installed on a computer, the model is then fed current input readings, and then the computer calculates an estimated value of the target instrument reading given the current system operating conditions. The current reading of the target instrument is then compared against its estimated value. Any deviations between the estimated value and the actual instrument value are analyzed for detecting calibration drifts or instrument failures.
In theory, the analytical redundancy method is highly effective. However, in practice the method has been impractical to execute. One problem associated with this technique is that the system model in many multi-element systems is extremely complicated. As a result, it is difficult to provide real-time processing of the information relating to the dissimilar instruments. Real-time predictions of element values are necessary for the analytical redundancy paradigm to be practical.
Another problem with the analytical redundancy method is generating an accurate model. The actual behavior of a non-linear system is not easy to reproduce. Prior art attempts to obtain accurate models for multi-element systems have been hindered by the complicated model and the expense of generating the model.
For the analytical redundancy method to become a practical tool, it is necessary to reduce the modelling and the processing time. In this regard, it would be helpful to automatically create the system model. Processing time may also be reduced by providing a method and apparatus for rapidly processing the system model data.
The automatic creation of the system model or rapid processing of the model data must operate within a number of constraints. First, the value of the target instrument should be "observable" from the input from the other instruments which are used to predict the target instrument value. In other words, an "observable" target instrument is an instrument whose output can be predicted from its coupling to other instruments in the system, where the coupling is established through the dynamics of the system.
In an observable system, the model inputs should collectively represent all the process parameters required to compute the output signal. In addition, the model should accurately represent the input-output relationship under various system conditions. The model should also account for any significant process dynamics. Finally, techniques should be available to parameterize the model to actual process conditions. This is necessary because plant equipment may not be operating at designed characteristics due to aging or other process conditions. The concept of observability is fully defined in linear dynamic system literature such as Takahashi, et al., Controls & Dynamic Systems, Addison-Wesley Publishing Co. (1970).
In contrast to the analytical redundancy method, statistical methods may be used to develop an input-output model using time history data. The advantage of the statistical method is that no mathematical description of the process is needed and the implementation is relatively simple. The disadvantage is that it is based on a "curve fit" to process data, and depending on the conditions under which these data are collected, and the selection of the input variables, they may not accurately represent all of the modeled process characteristics. In addition, most statistical techniques ignore dynamic relations between input and output, and are basically steady state models.
U.S. Pat. No. 5,023,045 discloses a plant malfunction diagnostic method which employs statistical methods. In particular, the '045 patent discloses a method of diagnosing the cause of a malfunction in a power plant. Once a malfunction is diagnosed, data relating to the conditions of the plant at the time of the malfunction are fed to a neural network to obtain an assessment of the cause of the malfunction. The neural network is trained on large sets of statistical data. As will be more fully demonstrated below, the '045 patent departs from the present invention in a number of respects. First, the disclosure relies upon statistical methods. In contrast, the present invention relies upon system modelling and analytical redundancy. The '045 patent includes a neural network which is trained on large sets of statistical data, as opposed to the reduced training set used with the present invention. The '045 patent endeavors to provide a solution to a system problem. In contrast, the present invention identifies a potential system problem. The present invention focuses upon testing individual component performance, as opposed to monitoring overall system performance.
Neural networks, as those disclosed in the '045 patent, emulate the ability of the human brain to identify patterns by simulating the operation of human nerve cells, called neurons. Artificial neural systems are networks of interconnected processing elements, or neurons. Each neuron can have multiple input signals, but generates only one output signal.
Neural networks typically include a number of layers. The first layer, the input layer, receives the input data, operates on it, and communicates the results to a hidden layer. After processing through one or more hidden layers, the signal is conveyed to the output layer. Each layer includes a group of neurons operating in parallel on the signals fed to the layer. The neurons in each group of neurons in a layer act as feature detectors. For instance, a group of neurons may act to identify conditions which result in excessive pressure in a pipe.
The feature detector function is realized through multiplying the input signal by a plurality of weight factors. The product is then summed by a summing network which applies its output through a function to generate the output of the neuron. The output of each neuron is therefore dependent upon the inputs applied to the neuron, the activation function, and the neuron's associated input signal weighting.
The weighting of the neuron inputs may be calculated so as to render the neuron sensitive to relationships between elements. A layer may have many groups of neurons which are processing different information in parallel. As will be demonstrated below, this neuron behavior can be used to simulate systems described by first-order ordinary differential equations by incorporating present and past system values.
By adjusting the weighting associated with the neurons, the network can become adaptive. That is, by readjusting the weights on the connections between the neurons in such a way that they generate a desired output for a given input, they can be used to provide an identifying output signal based upon the unknown input signal they receive. Thus, different system characteristics may be recognized by adapting the neural network to perform different logic functions and thereby respond to significant features which characterize a given operating condition of a system.
In order to make neural networks a feasible tool in recognition of failed elements within a system, the training of the neural networks should be based on causal relationships between the inputs and outputs for the operating conditions of the system. By relying upon causal relationships between the inputs and outputs, the neural networks are able to converge upon a solution. In contrast, with statistical methods, it is difficult for the neural networks to converge upon a solution. Moreover, even if convergence is obtained, there is some question as to causality since statistical methods are relied upon, as opposed to relationships between inputs and outputs.